Deep and Machine Learning for Microscopy
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Updated
Jun 26, 2024 - Python
Deep and Machine Learning for Microscopy
Explorative multivariate statistics in Python
Comparative analysis of pairwise interactions in multivariate time series.
A Python package housing a collection of deep-learning multi-modal data fusion method pipelines! From data loading, to training, to evaluation - fusilli's got you covered 🌸
Using fuzzy cognitive maps for multivariate data forecasting in Python 3.8.
(Multiblock) Partial Least Squares Regression for Python
Integrate your chemometric tools with the scikit-learn API 🧪 🤖
Python implementation of STATIS for analysis of several data tables
Several examples of multivariate techniques implemented in R, Python, and SAS. Multivariate concrete dataset retrieved from https://archive.ics.uci.edu/ml/datasets/Concrete+Slump+Test. Credit to Professor I-Cheng Yeh.
MFTE (Multi Feature Tagger of English) Python is the Python version based on Le Foll's MFTE written in Perl. It is extended to include semantic tags from Biber (2006) and Biber et al. (1999), including other specific tags.
Facial attractiveness in the brain: A multivariate pattern analysis
Python toolbox intended for GIS Archaeology tools developed by Matthew Bova
This repository presents a comprehensive analysis of sales representative profiles within a software product group. The analysis explores various factors at play such as age, gender, experience, personality type, certifications, feedback scores, salary, and Net Promoter Scores (NPS) to derive actionable insights and support decision-making process.
Correspondence Analysis with python
A mirror and a fork of PyChem
Analyze house price data using univariate, bivariate, and multivariate analysis.
Pipelines for Multivariate Analyses of Neuroimaging data in Python
Multivariate Linear and Logistic Regression Using Gradient Descent Optimization.
Parallel Coordinates visualization tool for n-D multivariate classifications
Iterative Amplitude Adjusted Fourier Transform with Simulated Annealing. Various properties kept during multivariate time series simulation by using a modified version of the IAAFT algorithm.
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